scholarly journals Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

2018 ◽  
Author(s):  
Alexander Genauck ◽  
Caroline Matthis ◽  
Milan Andrejevic ◽  
Lukas Ballon ◽  
Francesca Chiarello ◽  
...  
Keyword(s):  
Decision Making ◽  
Substance Abuse ◽  
Functional Connectivity ◽  
Gambling Disorder ◽  
Physiological Effect ◽  
Learning Processes ◽  
Addictive Disorders ◽  
Emotional Cues ◽  
Network Activation ◽  
Neural Signatures

Background: Just as substance use disorders (SUDs), gambling disorder (GD) is characterized by an increase in cue-dependent decision-making (similar to Pavlovian-to-instrumental transfer, PIT). PIT, as studied in SUDs and healthy subjects, is associated with altered communication between Nucleus Accumbens (NAcc), amygdala, and orbitofrontal cortex (OFC). These neural differences are, however, poorly understood. For example, it is unclear whether they are due to the physiological effects of substance abuse, or rather related to learning processes and/or other etiological factors like innate traits associated with addiction. We have thus investigated whether network activation patterns during a PIT task are also altered in GD, an addictive disorder not involving substance abuse. We have specifically studied which neural PIT patterns were best at distinguishing GD from HC subjects, all to improve our understanding of the neural signatures of GD and of addiction-related PIT in general. Methods: 30 GD and 30 HC subjects completed an affective decision-making task in a functional magnetic resonance imaging (fMRI) scanner. Gambling associated and other emotional cues were shown in the background during the task, allowing us to record multivariate neural PIT signatures focusing on a network of NAcc, amygdala and OFC. We built and tested a classifier based on these multivariate neural PIT signatures using cross-validated elastic net regression. Results and Discussion: As expected, GD subjects showed stronger PIT than HC subjects because they showed stronger increase in gamble acceptance when gambling cues were presented in the background. Classification based on neural PIT signatures yielded a significant AUC-ROC (0.70, p = 0.013). When inspecting the features of the classifier, we observed that GD showed stronger PIT-related functional connectivity between NAcc and amygdala elicited by gambling background cues, as well as between amygdala and OFC elicited by negative and positive cues. Conclusion: We propose that HC and GD subjects are distinguishable by PIT-related neural signatures including amygdala-NAcc-OFC functional connectivity. Our findings suggest that neural PIT alterations in addictive disorders might not depend on the physiological effect of a substance of abuse, but on related learning processes or even innate neural traits, also found in behavioral addictions.

scholarly journals Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls

2019 ◽  
Author(s):  
Alexander Genauck ◽  
Milan Andrejevic ◽  
Katharina Brehm ◽  
Caroline Matthis ◽  
Andreas Heinz ◽  
...  
Keyword(s):  
Decision Making ◽  
Loss Aversion ◽  
Gambling Disorder ◽  
Substance Dependence ◽  
Receiver Operating Curve ◽  
Addictive Disorders ◽  
Emotional Cues ◽  
Healthy Control ◽  
Induced Changes ◽  
Task Parameters

ABSTRACTWhile an increased impact of cues on decision-making has been associated with substance dependence, it is yet unclear whether this is also a phenotype of non-substance related addictive disorders, such as gambling disorder. To better understand the basic mechanisms of impaired decision-making in addiction, we investigated whether cue-induced changes in decision-making could distinguish gambling disorder (GD) from healthy control (HC) subjects. We expected that cue-induced changes in gamble acceptance and specifically in loss aversion would distinguish GD from HC subjects.30 GD subjects and 30 matched HC subjects completed a mixed gambles task where gambling and other emotional cues were shown in the background. We used machine learning and classification to carve out the importance of cue-dependency of decision-making and of loss aversion for distinguishing GD from HC subjects.Cross-validated classification yielded an area under the receiver operating curve (AUC-ROC) of 68.9% (p=0.002). Applying the classifier to an independent sample yielded an AUC-ROC of 65.0% (p=0.047). As expected, the classifier used cue-induced changes in gamble acceptance to distinguish GD from HC. Especially increased gambling during the presentation of gambling cues was characteristic of GD subjects. However, unexpectedly, cue-induced changes in loss aversion were irrelevant for distinguishing GD from HC subjects. To our knowledge, this is the first study to investigate the classificatory power of addiction-relevant behavioral task parameters when distinguishing GD from HC subjects. The results indicate that cue-induced changes in decision-making are a characteristic feature of addictive disorders, independent of a substance of abuse.

scholarly journals Decision-making inflexibility in a reversal learning task is associated with severity of problem gambling symptoms but not with a diagnosis of substance use disorder

2019 ◽  
Author(s):  
Maria Fernanda Jara-Rizzo ◽  
Juan F. Navas ◽  
Jose A. Rodas ◽  
José C. Perales
Keyword(s):  
Decision Making ◽  
Substance Use ◽  
Substance Use Disorder ◽  
Reversal Learning ◽  
Gambling Disorder ◽  
Learning Task ◽  
Learning Curves ◽  
Gambling Problems ◽  
Mixed Effect ◽  
Addictive Disorders

Background: Decisions made by individuals with disordered gambling are markedly inflexible. However, whether anomalies in learning from feedback are gambling-specific, or extend beyond gambling contexts, remains an open question. More generally, addictive disorders –including gambling disorder– have been proposed to be facilitated by individual differences in feedback-driven decision-making inflexibility, which has been studied in the lab with the Probabilistic Reversal Learning Task (PRLT). In this task, participants are first asked to learn which of two choice options is more advantageous, on the basis of trial-by-trial feedback, but, once preferences are established, reward contingencies are reversed, so that the advantageous option becomes disadvantageous and vice versa. Inflexibility is revealed by a less effective reacquisition of preferences after reversal, which can be distinguished from more generalized learning deficits.Methods: In the present study, we compared PRLT performance across two groups of 25 treatment-seeking patients diagnosed with an addictive disorder and who reported gambling problems, and 25 matched controls [18 Males/7 Females in both groups, Mage(SDage) = 25.24 (8.42) and 24.96 (7.90), for patients and controls, respectively]. Beyond testing for differences in the shape of PRLT learning curves across groups, the specific effect of problematic gambling symptoms’ severity was also assessed independently of group assignment. In order to surpass previous methodological problems, full acquisition and reacquisition curves were fitted using generalized mixed-effect models. Results: Results showed that (1) controls did not significantly differ from patients in global PRLT performance nor showed specific signs of decision-making inflexibility; and (2) regardless of whether group affiliation was controlled for or not, gambling severity was specifically associated with more inefficient learning in phases with reversed contingencies. Conclusion: Decision-making inflexibility, as revealed by difficulty to reacquire decisional preferences based on feedback after contingency reversals, seems to be associated with gambling problems, but not necessarily with a substance-use disorder diagnosis. This result aligns with gambling disorder models in which domain-general compulsivity is linked to vulnerability to develop gambling-specific problems with exposure to gambling opportunities.

scholarly journals Decision-making inflexibility in a reversal learning task is associated with severity of problem gambling symptoms but not with a diagnosis of substance use disorder

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
María F. Jara-Rizzo ◽  
Juan F. Navas ◽  
Jose A. Rodas ◽  
José C. Perales
Keyword(s):  
Decision Making ◽  
Substance Use ◽  
Substance Use Disorder ◽  
Reversal Learning ◽  
Gambling Disorder ◽  
Learning Task ◽  
Learning Curves ◽  
Gambling Problems ◽  
Mixed Effect ◽  
Addictive Disorders

Abstract Background Decisions made by individuals with disordered gambling are markedly inflexible. However, whether anomalies in learning from feedback are gambling-specific, or extend beyond gambling contexts, remains an open question. More generally, addictive disorders—including gambling disorder—have been proposed to be facilitated by individual differences in feedback-driven decision-making inflexibility, which has been studied in the lab with the Probabilistic Reversal Learning Task (PRLT). In this task, participants are first asked to learn which of two choice options is more advantageous, on the basis of trial-by-trial feedback, but, once preferences are established, reward contingencies are reversed, so that the advantageous option becomes disadvantageous and vice versa. Inflexibility is revealed by a less effective reacquisition of preferences after reversal, which can be distinguished from more generalized learning deficits. Methods In the present study, we compared PRLT performance across two groups of 25 treatment-seeking patients diagnosed with an addictive disorder and who reported gambling problems, and 25 matched controls [18 Males/7 Females in both groups, Mage(SDage) = 25.24 (8.42) and 24.96 (7.90), for patients and controls, respectively]. Beyond testing for differences in the shape of PRLT learning curves across groups, the specific effect of problematic gambling symptoms’ severity was also assessed independently of group assignment. In order to surpass previous methodological problems, full acquisition and reacquisition curves were fitted using generalized mixed-effect models. Results Results showed that (1) controls did not significantly differ from patients in global PRLT performance nor showed specific signs of decision-making inflexibility; and (2) regardless of whether group affiliation was controlled for or not, gambling severity was specifically associated with more inefficient learning in phases with reversed contingencies. Conclusion Decision-making inflexibility, as revealed by difficulty to reacquire decisional preferences based on feedback after contingency reversals, seems to be associated with gambling problems, but not necessarily with a substance-use disorder diagnosis. This result aligns with gambling disorder models in which domain-general compulsivity is linked to vulnerability to develop gambling-specific problems with exposure to gambling opportunities.

scholarly journals Neural signatures of experience-based improvements in deterministic decision-making

2016 ◽  
Vol 315 ◽  
pp. 51-65 ◽  
Author(s):  
Joshua J. Tremel ◽  
Patryk A. Laurent ◽  
David A. Wolk ◽  
Mark E. Wheeler ◽  
Julie A. Fiez
Keyword(s):  
Decision Making ◽  
Neural Signatures

Participative work design in lean production

2015 ◽  
Vol 27 (1) ◽  
pp. 19-33 ◽  
Author(s):  
Annika Lantz ◽  
Niklas Hansen ◽  
Conny Antoni
Keyword(s):  
Decision Making ◽  
Team Learning ◽  
Lean Production ◽  
Job Design ◽  
Learning Processes ◽  
Management Policy ◽  
Shop Floor ◽  
Content Type ◽  
Proactive Behaviour ◽  
The Relationship

Purpose – The purpose of this paper is to explore job design mechanisms that enhance team proactivity within a lean production system where autonomy is uttermost restricted. We propose and test a model where the team learning process of building shared meaning of work mediates the relationship between team participative decision-making, inter team relations and team proactive behaviour. Design/methodology/approach – The results are based on questionnaires to 417 employees within manufacturing industry (response rate 86 per cent) and managers’ ratings of team proactivity. The research model was tested by mediation analysis on aggregated data (56 teams). Findings – Team learning mediates the relationship between participative decision-making and inter team collaboration on team proactive behaviour. Input from stakeholders in the work flow and partaking in decisions about work, rather than autonomy in carrying out the work, enhance the teams’ proactivity through learning processes. Research limitations/implications – An investigation of the effects of different leadership styles and management policy on proactivity through team-learning processes might shed light on how leadership promotes proactivity, as results support the effects of team participative decision-making – reflecting management policy – on proactivity. Practical implications – Lean production stresses continuous improvements for enhancing efficiency, and such processes rely on individuals and teams that are proactive. Participation in forming the standardization of work is linked to managerial style, which can be changed and developed also within a lean concept. Based on our experiences of implementing the results in the production plant, we discuss what it takes to create and manage participative processes and close collaboration between teams on the shop floor, and other stakeholders such as production support, based on a shared understanding of the work and work processes. Social implications – Learning at the workplace is essential for long-term employability, and for job satisfaction and health. The lean concept is widely spread to both public bodies and enterprises, and it has been shown that it can be linked to increased stress and an increase in workload. Finding the potential for learning within lean production is essential for balancing the need of efficient production and employees’ health and well-being at work. Originality/value – Very few studies have investigated the paradox between lean and teamwork, yet many lean-inspired productions systems have teamwork as a pillar for enhancing effectiveness. A clear distinction between autonomy and participation contributes to the understanding of the links between job design, learning processes and team proactivity.

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.

scholarly journals Why hasn’t evolution selected for perfect self-control?

2018 ◽  
Author(s):  
Benjamin Hayden
Keyword(s):  
Decision Making ◽  
Cognitive Control ◽  
Learning Processes ◽  
Decision Makers ◽  
Self Control ◽  
Subjective Feeling ◽  
Obvious Reason ◽  
Control Failures

Self-control refers to the ability to deliberately reject tempting options and instead select ones that produce greater long-term benefits. Although some apparent failures of self-control are, on closer inspection, reward maximizing, at least some self-control failures are clearly disadvantageous and non-strategic. The existence of poor self-control presents an important evolutionary puzzle because there is no obvious reason why good self-control should be more costly than poor self-control. After all, a rock is infinitely patient. I propose that self-control failures result from cases in which well-learned (and thus routinized) decision making strategies yield suboptimal choices. These mappings persist in the decision-makers’ repertoire because they result from learning processes that are adaptive in the broader context, either on the timescale of learning or of evolution. Self-control, then, is a form of cognitive control and the subjective feeling of effort likely reflects the true costs of cognitive control. Poor self-control, in this view, is ultimately a result of bounded optimality.

scholarly journals Attenuated directed exploration during reinforcement learning in gambling disorder

2019 ◽  
Author(s):  
A. Wiehler ◽  
K. Chakroun ◽  
J. Peters
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Gambling Disorder ◽  
Brain Activity ◽  
Clinical Status ◽  
Classical Problem ◽  
Behavioral Flexibility ◽  
Network Connectivity ◽  
Prediction Errors ◽  
Reward Contingencies

AbstractGambling disorder is a behavioral addiction associated with impairments in decision-making and reduced behavioral flexibility. Decision-making in volatile environments requires a flexible trade-off between exploitation of options with high expected values and exploration of novel options to adapt to changing reward contingencies. This classical problem is known as the exploration-exploitation dilemma. We hypothesized gambling disorder to be associated with a specific reduction in directed (uncertainty-based) exploration compared to healthy controls, accompanied by changes in brain activity in a fronto-parietal exploration-related network.Twenty-three frequent gamblers and nineteen matched controls performed a classical four-armed bandit task during functional magnetic resonance imaging. Computational modeling revealed that choice behavior in both groups contained signatures of directed exploration, random exploration and perseveration. Gamblers showed a specific reduction in directed exploration, while random exploration and perseveration were similar between groups.Neuroimaging revealed no evidence for group differences in neural representations of expected value and reward prediction errors. Likewise, our hypothesis of attenuated fronto-parietal exploration effects in gambling disorder was not supported. However, during directed exploration, gamblers showed reduced parietal and substantia nigra / ventral tegmental area activity. Cross-validated classification analyses revealed that connectivity in an exploration-related network was predictive of clinical status, suggesting alterations in network dynamics in gambling disorder.In sum, we show that reduced flexibility during reinforcement learning in volatile environments in gamblers is attributable to a reduction in directed exploration rather than an increase in perseveration. Neuroimaging findings suggest that patterns of network connectivity might be more diagnostic of gambling disorder than univariate value and prediction error effects. We provide a computational account of flexibility impairments in gamblers during reinforcement learning that might arise as a consequence of dopaminergic dysregulation in this disorder.

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