scholarly journals Modeling variation in empathic sensitivity using go/no-go social reinforcement learning

2021 ◽  
Author(s):  
Katherine O'Connell ◽  
Marissa Walsh ◽  
Brandon Padgett ◽  
Sarah Connell ◽  
Abigail Marsh
Keyword(s):  
Reinforcement Learning ◽  
Learning Task ◽  
Social Contexts ◽  
Model Parameters ◽  
Social Reinforcement ◽  
Approach And Avoidance ◽  
Study Partner ◽  
The Subject ◽  
Considerable Individual Variation ◽  
Gains And Losses

Empathic experiences shape social behaviors and display considerable individual variation. Recent advances in computational behavioral modeling can help rigorously quantify individual differences, but remain understudied in the context of empathy and antisocial behavior. We adapted a go/no-go reinforcement learning task across social and non-social contexts such that monetary gains and losses explicitly impacted the subject, a study partner, or no one. Empathy was operationalized as sensitivity to others’ rewards, sensitivity to others’ losses, and as the Pavlovian influence of empathic outcomes on approach and avoidance behavior. Results showed that 61 subjects learned for a partner in a way that was computationally similar to how they learned for themselves. Results supported the psychometric value of individualized model parameters such as sensitivity to others’ loss, which was inversely associated with antisociality. Modeled empathic sensitivity also mapped onto motivation ratings, but was not associated with self-reported trait empathy. This work is the first to apply a social reinforcement learning task that spans affect and action requirement (go/no-go) to measure multiple facets of empathic sensitivity.

scholarly journals Explaining Valence Asymmetries in Value Learning: A Reinforcement Learning Account

2022 ◽  
Author(s):  
Chenxu Hao ◽  
Lilian E. Cabrera-Haro ◽  
Ziyong Lin ◽  
Patricia Reuter-Lorenz ◽  
Richard L. Lewis
Keyword(s):  
Reinforcement Learning ◽  
Downstream Processing ◽  
Learning Task ◽  
Model Parameters ◽  
Special Role ◽  
Learning Rates ◽  
The Asymmetry ◽  
Value Learning ◽  
Choice Policy ◽  
Better Than

To understand how acquired value impacts how we perceive and process stimuli, psychologists have developed the Value Learning Task (VLT; e.g., Raymond & O’Brien, 2009). The task consists of a series of trials in which participants attempt to maximize accumulated winnings as they make choices from a pair of presented images associated with probabilistic win, loss, or no-change outcomes. Despite the task having a symmetric outcome structure for win and loss pairs, people learn win associations better than loss associations (Lin, Cabrera-Haro, & Reuter-Lorenz, 2020). This asymmetry could lead to differences when the stimuli are probed in subsequent tasks, compromising inferences about how acquired value affects downstream processing. We investigate the nature of the asymmetry using a standard error-driven reinforcement learning model with a softmax choice rule. Despite having no special role for valence, the model yields the asymmetry observed in human behavior, whether the model parameters are set to maximize empirical fit, or task payoff. The asymmetry arises from an interaction between a neutral initial value estimate and a choice policy that exploits while exploring, leading to more poorly discriminated value estimates for loss stimuli. We also show how differences in estimated individual learning rates help to explain individual differences in the observed win-loss asymmetries, and how the final value estimates produced by the model provide a simple account of a post-learning explicit value categorization task.

scholarly journals Acute stress blunts prediction error signals in the dorsal striatum during reinforcement learning

2021 ◽  
Author(s):  
Joana Carvalheiro ◽  
Vasco A. Conceição ◽  
Ana Mesquita ◽  
Ana Seara-Cardoso
Keyword(s):  
Reinforcement Learning ◽  
Acute Stress ◽  
Dorsal Striatum ◽  
Learning Task ◽  
Neural Mechanisms ◽  
Prediction Errors ◽  
Functional Magnetic Resonance ◽  
Behavioural Performance ◽  
And Control ◽  
Gains And Losses

AbstractReinforcement learning, which implicates learning from the rewarding and punishing outcomes of our choices, is critical for adjusted behaviour. Acute stress seems to affect this ability but the neural mechanisms by which it disrupts this type of learning are still poorly understood. Here, we investigate whether and how acute stress blunts neural signalling of prediction errors during reinforcement learning using model-based functional magnetic resonance imaging. Male participants completed a well-established reinforcement learning task involving monetary gains and losses whilst under stress and control conditions. Acute stress impaired participants’ behavioural performance towards obtaining monetary gains, but not towards avoiding losses. Importantly, acute stress blunted signalling of prediction errors during gain and loss trials in the dorsal striatum — with subsidiary analyses suggesting that acute stress preferentially blunted signalling of positive prediction errors. Our results thus reveal a neurocomputational mechanism by which acute stress may impair reward learning.

scholarly journals Ventral striatum’s role in learning from gains and losses

2018 ◽  
Vol 115 (52) ◽  
pp. E12398-E12406 ◽  
Author(s):  
Craig A. Taswell ◽  
Vincent D. Costa ◽  
Elisabeth A. Murray ◽  
Bruno B. Averbeck
Keyword(s):  
Reinforcement Learning ◽  
Dopamine Release ◽  
Ventral Striatum ◽  
Learning Task ◽  
Specific Role ◽  
Aversive Stimuli ◽  
Learning Rates ◽  
Reinforcement Learning Models ◽  
Gains And Losses

Adaptive behavior requires animals to learn from experience. Ideally, learning should both promote choices that lead to rewards and reduce choices that lead to losses. Because the ventral striatum (VS) contains neurons that respond to aversive stimuli and aversive stimuli can drive dopamine release in the VS, it is possible that the VS contributes to learning about aversive outcomes, including losses. However, other work suggests that the VS may play a specific role in learning to choose among rewards, with other systems mediating learning from aversive outcomes. To examine the role of the VS in learning from gains and losses, we compared the performance of macaque monkeys with VS lesions and unoperated controls on a reinforcement learning task. In the task, the monkeys gained or lost tokens, which were periodically cashed out for juice, as outcomes for choices. They learned over trials to choose cues associated with gains, and not choose cues associated with losses. We found that monkeys with VS lesions had a deficit in learning to choose between cues that differed in reward magnitude. By contrast, monkeys with VS lesions performed as well as controls when choices involved a potential loss. We also fit reinforcement learning models to the behavior and compared learning rates between groups. Relative to controls, the monkeys with VS lesions had reduced learning rates for gain cues. Therefore, in this task, the VS plays a specific role in learning to choose between rewarding options.

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 A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

scholarly journals Boundary-Assisted Learning for Building Extraction from Optical Remote Sensing Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 760
Author(s):  
Sheng He ◽  
Wanshou Jiang
Keyword(s):  
Remote Sensing ◽  
Receptive Fields ◽  
Morphological Characteristics ◽  
Learning Task ◽  
Aerial Image ◽  
Model Parameters ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Convolutional Network ◽  
Remote Sensing Imagery

Deep learning methods have been shown to significantly improve the performance of building extraction from optical remote sensing imagery. However, keeping the morphological characteristics, especially the boundaries, is still a challenge that requires further study. In this paper, we propose a novel fully convolutional network (FCN) for accurately extracting buildings, in which a boundary learning task is embedded to help maintain the boundaries of buildings. Specifically, in the training phase, our framework simultaneously learns the extraction of buildings and boundary detection and only outputs extraction results while testing. In addition, we introduce spatial variation fusion (SVF) to establish an association between the two tasks, thus coupling them and making them share the latent semantics and interact with each other. On the other hand, we utilize separable convolution with a larger kernel to enlarge the receptive fields while reducing the number of model parameters and adopt the convolutional block attention module (CBAM) to boost the network. The proposed framework was extensively evaluated on the WHU Building Dataset and the Inria Aerial Image Labeling Dataset. The experiments demonstrate that our method achieves state-of-the-art performance on building extraction. With the assistance of boundary learning, the boundary maintenance of buildings is ameliorated.

scholarly journals Interrelation between the types of self-attitude and self-regulation of laziness in young individuals with different degree of self-regulation of behavior

2021 ◽  
pp. 28-39
Author(s):  
Alina Eduardovna Kim
Keyword(s):  
Young People ◽  
Self Regulation ◽  
Learning Task ◽  
Learning Activity ◽  
Work Process ◽  
Work Task ◽  
Work Tasks ◽  
The Subject ◽  
Theoretical Overview

The subject of this research is self-attitude and self-regulation of laziness in young individuals, who combine work and study. The article provides a brief theoretical overview of the research that prove interrelation between self-attitude and self-regulation of behavior and laziness. Using the quartilization procedure of the values of individual indicators, the author determined the groups with different degree of self-regulation of behavior; established the leading types of self-attitude of young individuals with different level of self-regulation of behavior. The presence and specificity of true links between the types of self-attitude with external and internal evaluative grounds and the severity of self-regulation of laziness in different contexts that provoke manifestations of laziness in young people with different level of self-regulation of behavior. Young individuals with high self-regulation of behavior demonstrate interconnectedness between self-regulation of laziness and types of self-attitude with internal evaluative grounds in execution of learning task, with external and internal evaluative grounds in execution of work task. The author underlines the importance of positive self-attitude for maintaining self-regulation of laziness. Interrelation between the types of self-attitude with both, external and internal evaluative grounds in execution of learning or work tasks are detected among the respondents with pronounced self-regulation above and below the average. Among young people with low self-regulation of behavior, the types of self-attitude with external evaluative grounds in conducting learning activity, the types of self-attitude with external and internal evaluative grounds in execution of work task, are interconnected with self-regulation of laziness. The reveled peculiarities should be taken into account in planning the educational and work process.

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