Feedback information and the reward positivity

Abstract An aberrant neural response to rewards has been linked to both depression and social anxiety. Most studies have focused on the neural response to monetary rewards, and few have tested different modalities of reward (e.g., social) that are more salient to particular forms of psychopathology. In addition, most studies contain critical confounds, including contrasting positive and negative feedback and failing to disentangle being correct from obtaining positive feedback. In the present study, 204 participants underwent electroencephalography during monetary and social feedback tasks that were matched in trial structure, timing, and feedback stimuli. The reward positivity (RewP) was measured in response to correctly identifying stimuli that resulted in monetary win, monetary loss, social like, or social dislike feedback. All monetary and social tasks elicited a RewP, which were positively correlated. Across all tasks, the RewP was negatively associated with depression and positively associated with social anxiety. The RewP to social dislike feedback, independent of monetary and social like feedback, was also associated with social anxiety. The present study suggests that a domain-general neural response to correct feedback demonstrates a differential association with depression and social anxiety, but a domain-specific neural response to social dislike feedback is uniquely associated with social anxiety.

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G-CNN and double-referenced thresholding for detecting time series anomalies

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200175 ◽

2020 ◽

pp. 1-12

Author(s):

Liping Li ◽

Zean Tian ◽

Kenli Li ◽

Cen Chen

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

Prediction Errors ◽

Feedback Information ◽

Public Datasets ◽

Gated Recurrent Units ◽

Control Feedback ◽

Intermediate Output ◽

Internet Company

Anomaly detection based on time series data is of great importance in many fields. Time series data produced by man-made systems usually include two parts: monitored and exogenous data, which respectively are the detected object and the control/feedback information. In this paper, a so-called G-CNN architecture that combined the gated recurrent units (GRU) with a convolutional neural network (CNN) is proposed, which respectively focus on the monitored and exogenous data. The most important is the introduction of a complementary double-referenced thresholding approach that processes prediction errors and calculates threshold, achieving balance between the minimization of false positives and the false negatives. The outstanding performance and extensive applicability of our model is demonstrated by experiments on two public datasets from aerospace and a new server machine dataset from an Internet company. It is also found that the monitored data is close associated with the exogenous data if any, and the interpretability of the G-CNN is discussed by visualizing the intermediate output of neural networks.

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Dissociating the effect of reward uncertainty and timing uncertainty on neural indices of reward prediction errors: A reward positivity (RewP) event-related potential (ERP) study

Biological Psychology ◽

10.1016/j.biopsycho.2021.108121 ◽

2021 ◽

pp. 108121

Author(s):

Alexandra M. Muir ◽

Addison C. Eberhard ◽

Megan S. Walker ◽

Angus Bennion ◽

Mikle South ◽

...

Keyword(s):

Event Related Potential ◽

Prediction Errors ◽

Reward Positivity ◽

Reward Prediction ◽

Timing Uncertainty

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Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems

Games ◽

10.3390/g12030062 ◽

2021 ◽

Vol 12 (3) ◽

pp. 62

Author(s):

Ralph S. Redden ◽

Greg A. Gagliardi ◽

Chad C. Williams ◽

Cameron D. Hassall ◽

Olave E. Krigolson

Keyword(s):

Reinforcement Learning ◽

Chess Player ◽

Reward Systems ◽

The Third ◽

Reward Positivity ◽

Eeg Data ◽

Attentional Engagement ◽

Level Of Difficulty ◽

Competitive Games ◽

Reinforcement Model

When we play competitive games, the opponents that we face act as predictors of the outcome of the game. For instance, if you are an average chess player and you face a Grandmaster, you anticipate a loss. Framed in a reinforcement learning perspective, our opponents can be thought of as predictors of rewards and punishments. The present study investigates whether facing an opponent would be processed as a reward or punishment depending on the level of difficulty the opponent poses. Participants played Rock, Paper, Scissors against three computer opponents while electroencephalographic (EEG) data was recorded. In a key manipulation, one opponent (HARD) was programmed to win most often, another (EASY) was made to lose most often, and the third (AVERAGE) had equiprobable outcomes of wins, losses, and ties. Through practice, participants learned to anticipate the relative challenge of a game based on the opponent they were facing that round. An analysis of our EEG data revealed that winning outcomes elicited a reward positivity relative to losing outcomes. Interestingly, our analysis of the predictive cues (i.e., the opponents’ faces) demonstrated that attentional engagement (P3a) was contextually sensitive to anticipated game difficulty. As such, our results for the predictive cue are contrary to what one might expect for a reinforcement model associated with predicted reward, but rather demonstrate that the neural response to the predictive cue was encoding the level of engagement with the opponent as opposed to value relative to the anticipated outcome.

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An Extenics-Based Scheduled Configuration Methodology for Low-Carbon Product Design in Consideration of Contradictory Problem Solving

Sustainability ◽

10.3390/su13115859 ◽

2021 ◽

Vol 13 (11) ◽

pp. 5859

Author(s):

Shedong Ren ◽

Fangzhi Gui ◽

Yanwei Zhao ◽

Min Zhan ◽

Wanliang Wang ◽

...

Keyword(s):

Problem Solving ◽

Product Design ◽

Information Flow ◽

Element Model ◽

Flow Diagram ◽

Directed Network ◽

Low Carbon ◽

Carbon Product ◽

Feedback Information ◽

Configuration Methodology

Low-carbon product design involves a redesign process that requires not only structural module modification, but more importantly, generating innovative principles to solve design contradictions. Such contradictions include when current design conditions cannot satisfy design requirements or there are antithetical design goals. On the other hand, configuration tasks in the reconfiguration process are interdependent, which requires a well-scheduled arrangement to reduce feedback information. This study proposes an effective configuration methodology for low-carbon design. Firstly, configuration tasks and configuration parameters are designated through quality characteristics, and the directed network along with the associated values of configuration tasks are transformed into the design structure matrix to construct the information flow diagram. Then, the Extenics-based problem-solving model is presented to address design contradictions: low-carbon incompatibility and antithetical problems are clarified and formulated with a basic-element model; extensible and conjugate analysis tools are used to identify problematic structures and provide feasible measures; the Gantt chart of measures execution based on the information flow diagram is constructed to reduce feedback and generate robust schemes with strategy models. The methodology is applied to the vacuum pump low-carbon design, the results show that it effectively solves contradictions with innovative design schemes, and comparative analysis verifies the performance of Extenics.

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Feedback, Information, and the Fabrication of Spatialisme

MLN ◽

10.1353/mln.2020.0056 ◽

2020 ◽

Vol 135 (4) ◽

pp. 859-887

Author(s):

Marc Kohlbry

Keyword(s):

Feedback Information

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