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Equiprobable Go/NoGo learning task (i.e. the ratio Go cues: NoGo cues is 50:50) featuring Win cues (chance for winning points/ money vs. neutral outcome) and Avoid cues (chance for neutral outcome vs. losing points/money).
The task features 4 conditions (at least 1 cue per condition):
- Go-to-Win: Cue with chance for winning money, requires Go response
- Go-to-Avoid: Cue with chance for losing money, requires Go response
- NoGo-to-Win: Cue with chance for winning money, requires NoGo response
- NoGo-to-Avoid: Cue with chance for losing money, requires NoGo response
All conditions feature the same amount of trials, i.e. Go/NoGo and Win/Avoid are fully orthogonalized (unlike other go/nogo tasks that are aimed at measuring inhibition and thus feature more go trials).

The correct response (Go/NoGo) is not instructed, but has been learned by trial-and-error from feedback.
Cue valence (Win/Avoid) is either instructed (e.g. cue edges in certain color) or has to be inferred from feedback (only Win cues can yield winning money, only Avoid cues can yield losing money).
Outcomes are usually probabilistic (e.g. 80% valid feedback, i.e. correct responses lead to winning money for Win cues/ neutral outcomes for Avoid cues in only 80% of trials, otherwise to invalid feedback; reversed probabilities for incorrect responses).

The task is used to measure Pavlovian biases/ motivational biases, i.e. the tendency to show more Go responses (and faster reaction times) to Win cues than Avoid cues.



Definition contributed by JAlgermissen
motivational go/no-go learning task has been asserted to measure the following CONCEPTS
as measured by the contrast:




as measured by the contrast:




as measured by the contrast:




Phenotypes associated with motivational go/no-go learning task

Disorders

No associations have been added.

Traits

No associations have been added.

Behaviors

No associations have been added.


IMPLEMENTATIONS of motivational go/no-go learning task
No implementations have been added.
EXTERNAL DATASETS for motivational go/no-go learning task
Frontal network dynamics reflect neurocomputational mechanisms for reducing maladaptive biases in motivated action
Predicting the wide-ranging effects of enhancing dopamine on cognition
CONDITIONS

Experimental conditions are the subsets of an experiment that define the relevant experimental manipulation.

CONTRASTS
Conflict(edit)
Condition Weight
Go-to-Avoid 1.0
Go-to-Win -1.0
NoGo-to-Win 1.0
NoGo-to-Avoid -1.0
Action(edit)
Condition Weight
Go-to-Avoid 1.0
Go-to-Win 1.0
NoGo-to-Win -1.0
NoGo-to-Avoid -1.0
Valence(edit)
Condition Weight
Go-to-Avoid -1.0
Go-to-Win 1.0
NoGo-to-Win 1.0
NoGo-to-Avoid -1.0

In the Cognitive Atlas, we define a contrast as any function over experimental conditions. The simplest contrast is the indicator value for a specific condition; more complex contrasts include linear or nonlinear functions of the indicator across different experimental conditions.

INDICATORS
reaction time
response (Go/ NoGo)

An indicator is a specific quantitative or qualitative variable that is recorded for analysis. These may include behavioral variables (such as response time, accuracy, or other measures of performance) or physiological variables (including genetics, psychophysiology, or brain imaging data).

Term BIBLIOGRAPHY

The impact of traumatic stress on Pavlovian biases
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Psychological Medicine
2018-01-01

Suicidal thoughts and behaviors are associated with an increased decision-making bias for active responses to escape aversive states.
Alexander J. Millner, Hanneke E. M. den Ouden, Samuel J. Gershman, Catherine R. Glenn, Jaclyn C. Kearns, Aaron M. Bornstein, Brian P. Marx, Terence M. Keane and Matthew K. Nock
Journal of Abnormal Psychology
2019-02-01

Dopamine Selectively Modulates the Outcome of Learning Unnatural Action–Valence Associations
Nelleke C. Van Wouwe, Daniel O. Claassen, Joseph S. Neimat, Kristen E. Kanoff and Scott A. Wylie
Journal of Cognitive Neuroscience
2017-05-01

Easy to learn, hard to suppress: The impact of learned stimulus–outcome associations on subsequent action control
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Brain and Cognition
2015-12-01

Adolescents exhibit reduced Pavlovian biases on instrumental learning
Hillary A. Raab and Catherine A. Hartley
Scientific Reports
2020-12-01

Asymmetric coupling of action and outcome valence in active and observational feedback learning
Jutta Peterburs, Alena Frieling and Christian Bellebaum
Psychological Research
2021-06-01

The impact of social anxiety on feedback-based go and nogo learning
Jutta Peterburs, Christine Albrecht and Christian Bellebaum
Psychological Research
2021-02-01

Neural activity and fundamental learning, motivated by monetary loss and reward, are intact in mild to moderate major depressive disorder
Michael Moutoussis, Robb B. Rutledge, Gita Prabhu, Louise Hrynkiewicz, Jordan Lam, Olga-Therese Ousdal, Marc Guitart-Masip, Peter Fonagy, Raymond J. Dolan and Jean Daunizeau
PLOS ONE
2018-08-02

Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood
Michael Moutoussis, Edward T. Bullmore, Ian M. Goodyer, Peter Fonagy, Peter B. Jones, Raymond J. Dolan, Peter Dayan and Samuel J. Gershman
PLOS Computational Biology
2018-12-31

Pavlovian Control of Escape and Avoidance
Alexander J. Millner, Samuel J. Gershman, Matthew K. Nock and Hanneke E. M. den Ouden
Journal of Cognitive Neuroscience
2018-10-01

Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task
Anne Kühnel, Vanessa Teckentrup, Monja P. Neuser, Quentin J.M. Huys, Caroline Burrasch, Martin Walter and Nils B. Kroemer
European Neuropsychopharmacology
2020-06-01

Enhanced Go and NoGo Learning in Individuals With Obesity
Jana Kube, Kathleen Wiencke, Sandra Hahn, Arno Villringer and Jane Neumann
Frontiers in Behavioral Neuroscience
2020-02-14

Expecting the good: Symbolic valence signals provoke action biases and undermine goal-directed behavior
Vincent Hoofs, Arthur Prével and Ruth M. Krebs
Acta Psychologica
2020-05-01

Overcoming Pavlovian bias in semantic space
Sam Ereira, Marine Pujol, Marc Guitart-Masip, Raymond J. Dolan and Zeb Kurth-Nelson
Scientific Reports
2021-12-01

Intermittent Absence of Control during Reinforcement Learning Interferes with Pavlovian Bias in Action Selection
Gábor Csifcsák, Eirik Melsæter and Matthias Mittner
Journal of Cognitive Neuroscience
2020-04-01

Dorsal striatal dopamine D1 receptor availability predicts an instrumental bias in action learning
Lieke de Boer, Jan Axelsson, Rumana Chowdhury, Katrine Riklund, Raymond J. Dolan, Lars Nyberg, Lars Bäckman and Marc Guitart-Masip
Proceedings of the National Academy of Sciences
2019-01-02

Controllability governs the balance between Pavlovian and instrumental action selection
Hayley M. Dorfman and Samuel J. Gershman
Nature Communications
2019-12-01

Differential, but not opponent, effects of l-DOPA and citalopram on action learning with reward and punishment
Marc Guitart-Masip, Marcos Economides, Quentin J. M. Huys, Michael J. Frank, Rumana Chowdhury, Emrah Duzel, Peter Dayan and Raymond J. Dolan
Psychopharmacology
2014-03-01

Go and no-go learning in reward and punishment: Interactions between affect and effect
Marc Guitart-Masip, Quentin J.M. Huys, Lluis Fuentemilla, Peter Dayan, Emrah Duzel and Raymond J. Dolan
NeuroImage
2012-08-01

Action Dominates Valence in Anticipatory Representations in the Human Striatum and Dopaminergic Midbrain
M. Guitart-Masip, L. Fuentemilla, D. R. Bach, Q. J. M. Huys, P. Dayan, R. J. Dolan and E. Duzel
Journal of Neuroscience
2011-05-25

Frontal Theta Overrides Pavlovian Learning Biases
J. F. Cavanagh, I. Eisenberg, M. Guitart-Masip, Q. Huys and M. J. Frank
Journal of Neuroscience
2013-05-08

Catecholaminergic challenge uncovers distinct Pavlovian and instrumental mechanisms of motivated (in)action
Jennifer C Swart, Monja I Froböse, Jennifer L Cook, Dirk EM Geurts, Michael J Frank, Roshan Cools and Hanneke EM den Ouden
eLife
2017-05-15

Frontal network dynamics reflect neurocomputational mechanisms for reducing maladaptive biases in motivated action
Jennifer C. Swart, Michael J. Frank, Jessica I. Määttä, Ole Jensen, Roshan Cools, Hanneke E. M. den Ouden and Marios Philiastides
PLOS Biology
2018-10-18

Effects of dopamine on reinforcement learning in Parkinson’s disease depend on motor phenotype
Annelies J van Nuland, Rick C Helmich, Michiel F Dirkx, Heidemarie Zach, Ivan Toni, Roshan Cools and Hanneke E M den Ouden
Brain
2020-11-01