scholarly journals Rumination Derails Reinforcement Learning With Possible Implications for Ineffective Behavior

2021 ◽  
pp. 216770262110513
Author(s):  
Peter Hitchcock ◽  
Evan Forman ◽  
Nina Rothstein ◽  
Fengqing Zhang ◽  
John Kounios ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Task ◽  
Decay Rates ◽  
Impaired Performance ◽  
Attentional Scope ◽  
Within Subjects ◽  
Attentional Breadth ◽  
Performance Results ◽  
Scope Model ◽  
After Error

How does rumination affect reinforcement learning—the ubiquitous process by which people adjust behavior after error to behave more effectively in the future? In a within-subjects design ( N = 49), we tested whether experimentally manipulated rumination disrupts reinforcement learning in a multidimensional learning task previously shown to rely on selective attention. Rumination impaired performance, yet unexpectedly, this impairment could not be attributed to decreased attentional breadth (quantified using a decay parameter in a computational model). Instead, trait rumination (between subjects) was associated with higher decay rates (implying narrower attention) but not with impaired performance. Our task-performance results accord with the possibility that state rumination promotes stress-generating behavior in part by disrupting reinforcement learning. The trait-rumination finding accords with the predictions of a prominent model of trait rumination (the attentional-scope model). More work is needed to understand the specific mechanisms by which state rumination disrupts reinforcement learning.

scholarly journals Rumination Derails Reinforcement Learning with Possible Implications for Ineffective Behavior

2021 ◽  
Author(s):  
Peter Hitchcock ◽  
Evan Forman ◽  
Nina Jill Rothstein ◽  
Fengqing Zhang ◽  
John Kounios ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Task ◽  
Decay Rates ◽  
Impaired Performance ◽  
Subject Design ◽  
Attentional Scope ◽  
Attentional Breadth ◽  
Performance Results ◽  
Scope Model ◽  
After Error

How does rumination affect reinforcement learning—the ubiquitous process by which we adjust behavior after error in order to behave more effectively in the future? In a within-subject design (n=49), we tested whether experimentally induced rumination disrupts reinforcement learning in a multidimensional learning task previously shown to rely on selective attention. Rumination impaired performance, yet unexpectedly this impairment could not be attributed to decreased attentional breadth (quantified using a “decay” parameter in a computational model). Instead, trait rumination (between subjects) was associated with higher decay rates (implying narrower attention), yet not with impaired performance. Our task-performance results accord with the possibility that state rumination promotes stress-generating behavior in part by disrupting reinforcement learning. The trait-rumination finding accords with the predictions of a prominent model of trait rumination (the attentional-scope model). More work is needed to understand the specific mechanisms by which state rumination disrupts reinforcement learning.

Neuroevolution approach for reinforcement learning task applied to control system of a cart pole system

2019 ◽  
Author(s):  
Marco Boaretto ◽  
Gabriel Chaves Becchi ◽  
Luiza Scapinello Aquino ◽  
Aderson Cleber Pifer ◽  
Helon Vicente Hultmann Ayala ◽  
...  
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Learning Task ◽  
Pole System

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

scholarly journals The rational use of causal inference to guide reinforcement learning strengthens with age

2019 ◽  
Author(s):  
Alexandra O. Cohen ◽  
Kate Nussenbaum ◽  
Hayley Dorfman ◽  
Samuel J. Gershman ◽  
Catherine A. Hartley
Keyword(s):  
Reinforcement Learning ◽  
Causal Structure ◽  
Learning Task ◽  
Negative Events ◽  
Shape Learning ◽  
Adolescents And Adults ◽  
Bayesian Reinforcement Learning ◽  
External Causes ◽  
Reinforcement Learning Models ◽  
Best Fit

Beliefs about the controllability of positive or negative events in the environment can shape learning throughout the lifespan. Previous research has shown that adults’ learning is modulated by beliefs about the causal structure of the environment such that they will update their value estimates to a lesser extent when the outcomes can be attributed to hidden causes. The present study examined whether external causes similarly influenced outcome attributions and learning across development. Ninety participants, ages 7 to 25 years, completed a reinforcement learning task in which they chose between two options with fixed reward probabilities. Choices were made in three distinct environments in which different hidden agents occasionally intervened to generate positive, negative, or random outcomes. Participants’ beliefs about hidden-agent intervention aligned well with the true probabilities of positive, negative, or random outcome manipulation in each of the three environments. Computational modeling of the learning data revealed that while the choices made by both adults (ages 18 - 25) and adolescents (ages 13 - 17) were best fit by Bayesian reinforcement learning models that incorporate beliefs about hidden agent intervention, those of children (ages 7 - 12) were best fit by a one learning rate model that updates value estimates based on choice outcomes alone. Together, these results suggest that while children demonstrate explicit awareness of the causal structure of the task environment they do not implicitly use beliefs about the causal structure of the environment to guide reinforcement learning in the same manner as adolescents and adults.

scholarly journals Gaze as a Window to the Process of Novel Adjective Mapping

Languages ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 33
Author(s):  
Hanako Yoshida ◽  
Aakash Patel ◽  
Joseph Burling
Keyword(s):  
Learning Task ◽  
Target Object ◽  
Attention Allocation ◽  
Correct Item ◽  
New Words ◽  
Knowledge Account ◽  
Category Knowledge ◽  
Children's Attention ◽  
Performance Results

This study evaluated two explanations for how learning of novel adjectives is facilitated when all the objects are from the same category (e.g., exemplar and testing objects are all CUPS) and the object category is a known to the children. One explanation (the category knowledge account) focuses on early knowledge of syntax–meaning correspondence, and another (the attentional account) focuses on the role of repeated perceptual properties. The first account presumes implicit understanding that all the objects belong to the same category, and the second account presumes only that redundant perceptual experiences minimize distraction from irrelevant features and thus guide children’s attention directly to the correct item. The present study tests the two accounts by documenting moment-to-moment attention allocation (e.g., looking at experimenter’s face, exemplar object, target object) during a novel adjective learning task with 50 3-year-olds. The results suggest that children’s attention was guided directly to the correct item during the adjective mapping and that such direct attention allocation to the correct item predicted children’s adjective mapping performance. Results are discussed in relation to their implication for children’s active looking as the determinant of process for mapping new words to their meanings.

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