Sticky Me: Self-Relevance Slows Reinforcement Learning

Marius Golubickis* (Corresponding Author), Colin Macrae

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


A prominent facet of social-cognitive functioning is that self-relevant information is prioritized in perception, attention, and memory. What is not yet understood, however, is whether similar effects arise during learning. In particular, compared to other people (e.g., best friend), is information about the self acquired more rapidly? To explore this matter, here we used a probabilistic selection task in combination with computational modeling (i.e., Reinforcement Learning Drift Diffusion Model analysis) to establish how self-relevance influences learning under conditions of uncertainty (i.e., choices are based on the perceived likelihood of positive and negative outcomes). Across two experiments, a consistent pattern of effects was observed. First, learning rates for both positive and negative prediction errors were slower for self-relevant compared to friend-relevant associations. Second, self-relevant (vs. friend-relevant) learning was characterized by the exploitation (vs. exploration) of choice selections. That is, in a complex (i.e., probabilistic) decision-making environment, previously rewarded self-related outcomes were selected more often than novel — but potentially riskier — alternatives. The implications of these findings for accounts of self-function are considered.

Original languageEnglish
Article number105207
Number of pages10
Early online date22 Jun 2022
Publication statusPublished - Oct 2022


  • self
  • self-prioritization
  • learning
  • probabilistic selection task
  • reinforcement learning drift diffusion model.


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