Learning About the Self: Motives for coherence and positivity constrain learning from self-relevant feedback

Mapping Intimacies ◽

10.31234/osf.io/etz56 ◽

2021 ◽

Author(s):

Jacob Elder ◽

Tyler Davis ◽

Brent Hughes

Keyword(s):

Reinforcement Learning ◽

Negative Feedback ◽

Positive Feedback ◽

Mechanistic Explanation ◽

The Self ◽

Self Concept ◽

Causal Network ◽

Social Feedback ◽

Learning Framework ◽

Self Learning

People learn about themselves from social feedback, but desires for coherence and positivity constrain how feedback is incorporated into the self-concept. We develop a network-based model of the self-concept and embed it in a reinforcement learning framework to provide a mechanistic account of how motivations shape self-learning from feedback. Participants (n = 46) received feedback while self-evaluating on traits drawn from a causal network of trait semantics. Network-defined communities were assigned different likelihoods of positive feedback. Participants learned from positive feedback but dismissed negative feedback, as reflected by asymmetries in computational parameters that represent the incorporation of positive versus negative outcomes. Furthermore, participants were constrained in how they incorporated feedback: self-evaluations changed less for traits more important to coherence of the network. We provide a mechanistic explanation of how motives for coherence and positivity jointly constrain learning about the self from feedback that makes testable predictions for future clinical research.

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High-Fidelity Depth Upsampling Using the Self-Learning Framework

Sensors ◽

10.3390/s19010081 ◽

2018 ◽

Vol 19 (1) ◽

pp. 81

Author(s):

Inwook Shim ◽

Tae-Hyun Oh ◽

In Kweon

Keyword(s):

Stereo Matching ◽

Depth Map ◽

The Self ◽

Sensor Data ◽

High Fidelity ◽

Learning Framework ◽

Depth Maps ◽

Potential Applications ◽

Self Learning

This paper presents a depth upsampling method that produces a high-fidelity dense depth map using a high-resolution RGB image and LiDAR sensor data. Our proposed method explicitly handles depth outliers and computes a depth upsampling with confidence information. Our key idea is the self-learning framework, which automatically learns to estimate the reliability of the upsampled depth map without human-labeled annotation. Thereby, our proposed method can produce a clear and high-fidelity dense depth map that preserves the shape of object structures well, which can be favored by subsequent algorithms for follow-up tasks. We qualitatively and quantitatively evaluate our proposed method by comparing other competing methods on the well-known Middlebury 2014 and KITTIbenchmark datasets. We demonstrate that our method generates accurate depth maps with smaller errors favorable against other methods while preserving a larger number of valid points, as we also show that our approach can be seamlessly applied to improve the quality of depth maps from other depth generation algorithms such as stereo matching and further discuss potential applications and limitations. Compared to previous work, our proposed method has similar depth errors on average, while retaining at least 3% more valid depth points.

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Stuck in a negative me: fMRI study on the role of disturbed self-views in social feedback processing in borderline personality disorder

Psychological Medicine ◽

10.1017/s0033291719000448 ◽

2019 ◽

Vol 50 (4) ◽

pp. 625-635 ◽

Cited By ~ 2

Author(s):

Charlotte C. van Schie ◽

Chui-De Chiu ◽

Serge A. R. B. Rombouts ◽

Willem J. Heiser ◽

Bernet M. Elzinga

Keyword(s):

Borderline Personality Disorder ◽

Personality Disorder ◽

Negative Feedback ◽

Positive Feedback ◽

Borderline Personality ◽

Positive Interactions ◽

Social Feedback ◽

Feedback Processing ◽

Temporoparietal Junction ◽

Fmri Study

AbstractBackgroundInterpersonal difficulties in borderline personality disorder (BPD) could be related to the disturbed self-views of BPD patients. This study investigates affective and neural responses to positive and negative social feedback (SF) of BPD patients compared with healthy (HC) and low self-esteem (LSE) controls and how this relates to individual self-views.MethodsBPD (N = 26), HC (N = 32), and LSE (N = 22) performed a SF task in a magnetic resonance imaging scanner. Participants received 15 negative, intermediate and positive evaluative feedback words putatively given by another participant and rated their mood and applicability of the words to the self.ResultsBPD had more negative self-views than HC and felt worse after negative feedback. Applicability of feedback was a less strong determinant of mood in BPD than HC. Increased precuneus activation was observed in HC to negative compared with positive feedback, whereas in BPD, this was similarly low for both valences. HC showed increased temporoparietal junction (TPJ) activation to positive v. negative feedback, while BPD showed more TPJ activation to negative feedback. The LSE group showed a different pattern of results suggesting that LSE cannot explain these findings in BPD.ConclusionsThe negative self-views that BPD have, may obstruct critically examining negative feedback, resulting in lower mood. Moreover, where HC focus on the positive feedback (based on TPJ activation), BPD seem to focus more on negative feedback, potentially maintaining negative self-views. Better balanced self-views may make BPD better equipped to deal with potential negative feedback and more open to positive interactions.

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FREQUENCY OF SOCIAL EVALUATION IN SELF-OBSERVED DAILY INTERACTIONS

Social Behavior and Personality An International Journal ◽

10.2224/sbp.1983.11.1.77 ◽

1983 ◽

Vol 11 (1) ◽

pp. 77-80 ◽

Cited By ~ 2

Author(s):

Thomas J. Schoeneman

Keyword(s):

Social Interaction ◽

Negative Feedback ◽

Positive Feedback ◽

Family Members ◽

Social Feedback ◽

Social Evaluation ◽

Daily Interactions ◽

At Home

This study evaluated the widely-held assumption that social evaluations (and especially negative feedback) are infrequent in daily interactions. Whereas previous investigations have asked about evaluative interactions using a one-sitting questionnaire format, this research requested undergraduates to self-observe, in a structured way, five different hours of social interaction and to report on sources and content of social feedback. Instances of evaluation were counted and judged as being positive or negative feedback. Participants reported an average of 2.6 evaluations per rated hour of interaction. Of the reports that were clearly classifiable as positive or negative feedback, an average of 61.4% were rated as positive. Students living at home with family members reported fewer instances of positive feedback (51.3%) than those living away from home (70.1%), and family members gave positive evaluations more infrequently (39.1%) than did friends (64.6%) and all other evaluators (66.2%). Limitations and implications of these findings are discussed briefly.

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Creation and implementation of a set of game strategies based on training neural networks with reinforcement learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/2134/1/012005 ◽

2021 ◽

Vol 2134 (1) ◽

pp. 012005

Author(s):

D S Kozlov ◽

O N Polovikova

Keyword(s):

Neural Networks ◽

Reinforcement Learning ◽

The Self ◽

Learning Approach ◽

Self Learning

Abstract The study explores the problems of reinforcement learning and finding non-obvious play strategies using reinforcement learning. Two approaches to agent training (blind and pattern-based) are considered and implemented. The advantage of the self-learning approach with reinforcement using patterns as applied to a specific game (tic-tac-toe five in a row) is shown. Recorded and analyzed the use of unusual strategies by an agent using a pattern-based approach.

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Neuro-fuzzy-based skill learning for robots

Robotica ◽

10.1017/s026357471100124x ◽

2011 ◽

Vol 30 (6) ◽

pp. 1013-1027 ◽

Cited By ~ 2

Author(s):

Hsien-I. Lin ◽

C. S. George Lee

Keyword(s):

Reinforcement Learning ◽

Skill Learning ◽

The Self ◽

Stimulus Response ◽

Learning Framework ◽

Significant Stimulus ◽

Neuro Fuzzy ◽

Skill Decomposition ◽

Fuzzy Network ◽

Self Organizing

SUMMARYEndowing robots with the ability of skill learning enables them to be versatile and skillful in performing various tasks. This paper proposes a neuro-fuzzy-based, self-organizing skill-learning framework, which differs from previous work in its capability of decomposing a skill by self-categorizing it into significant stimulus-response units (SRU, a fundamental unit of our skill representation), and self-organizing learned skills into a new skill. The proposed neuro-fuzzy-based, self-organizing skill-learning framework can be realized by skill decomposition and skill synthesis. Skill decomposition aims at representing a skill and acquiring it by SRUs, and is implemented by stages with a five-layer neuro-fuzzy network with supervised learning, resolution control, and reinforcement learning to enable robots to identify a sufficient number of significant SRUs for accomplishing a given task without extraneous actions. Skill synthesis aims at organizing a new skill by sequentially planning learned skills composed of SRUs, and is realized by stages, which establish common SRUs between two similar skills and self-organize a new skill from these common SRUs and additional new SRUs by reinforcement learning. Computer simulations and experiments with a Pioneer 3-DX mobile robot were conducted to validate the self-organizing capability of the proposed skill-learning framework in identifying significant SRUs from task examples and in common SRUs between similar skills and learning new skills from learned skills.

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