Self-utility distance as a computational approach to understanding self-concept clarity

Communications Psychology, Journal Year: 2025, Volume and Issue: 3(1)

Published: March 25, 2025

Abstract Self-concept stability and cohesion are crucial for psychological functioning well-being, yet the mechanisms that underpin this fundamental aspect of human cognition remain underexplored. Integrating insights from cognitive personality psychology with reinforcement learning, we introduce Self-Utility Distance (SUD)—a metric quantifying dissimilarities between individuals’ self-concept attributes their expected utility value. In Study 1 ( n = 155), participants provided self- ratings using a set predefined adjectives. SUD showed significant negative relationship Self-Concept Clarity persisted after accounting Self-Esteem. 2 323), found provides incremental predictive accuracy over Ideal-Self Ought-Self discrepancies in prediction Clarity. 3 85), investigated mechanistic principles underlying Distance. Participants conducted social learning task where they learned about trait utilities reference group. We formalized different computational models to investigate strategies individuals use adjust estimates response environmental feedback. Through Hierarchical Bayesian Inference, evidence utilized modulate effectively avoiding maximization Our findings provide into dynamics might help understand maintenance adaptive maladaptive traits.

Language: Английский

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Old strategies, new environments: Reinforcement Learning on social media

Published: May 22, 2024

The rise of social media has dramatically altered the world – introducing new behaviours which can satisfy our needs. However, it is yet unknown whether human strategies, are well-adapted to offline we developed in, operate as effectively within this environment. Here, describe how computational framework Reinforcement Learning help us precisely frame problem and diagnose where behaviour-environment mismatches emerge. describes a process by an agent learn maximise their long-term reward. Learning, proven successful in characterising behaviour, consists three stages: updating expected reward, valuating reward integrating subjective costs such effort, selecting action. Specific affordances, quantifiability feedback, might interact with at each these stages. In some cases, affordances exploit biases beneficial offline, violating environmental conditions under optimal for example, when algorithmic personalisation content interacts confirmation bias. Characterising impact specific aspects through lens improve understanding digital environments shape behaviour. Indeed, formal could address pressing open questions about use, including its changing role across development, on outcomes mental health.

Language: Английский

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Old strategies, new environments: Reinforcement Learning on social media

Biological Psychiatry, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

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Network Footprints: A Laboratory Experiment on Brokerage and Information Diffusion

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

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Language: Английский

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Social content is prioritized in episodic memory

Published: May 24, 2024

Episodic memory helps facilitate navigation of the social world. Yet, whether content is prioritized when making decisions involving episodic unclear. Testing elements are retrieving multi-element episodes used for and non-social decision-making, online volunteers encoded episode triplets comprising a location, activity, clique (i.e. group) that related to decision cue was either person or an object. Subsequent associative tests in all tasks revealed enhanced recall activity pairings with cliques. Additionally, task contexts further boosted event linked same cue. Computational modeling retrieval response times these effects were consistent more holistic pattern completion-like recalling content. These results imply holds privileged role over other details memory, while offering putative mechanism prioritization processes.

Language: Английский

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Neural responses to social rejection reflect dissociable learning about relational value and reward

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(49)

Published: Nov. 26, 2024

Social rejection hurts, but it can also be informative: Through experiences of acceptance and rejection, people identify partners interested in connecting with them choose which ties to cement or sever. What is that actually learn from rejection? In social interactions, two kinds information. First, generally rewarding outcomes, may include concrete opportunities for interaction. Second, track the “relational value” others ascribe them—an internal model how much value them. Here, we used computational neuroimaging dissociate these forms learning. Participants repeatedly tried match a game. Feedback revealed whether they successfully matched (a outcome) other person wanted play (relational value). A Bayesian cognitive participants chose who provided outcomes valued Whereas learning was linked brain regions involved reward-based reinforcement, about relational previously associated rejection. These findings precise computations underlying responses support neurocomputational affiliation build an outcomes.

Language: Английский

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The Intentional Stance Drives Relational Reasoning in Social Observation

Published: Jan. 1, 2023

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Language: Английский

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