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Contributed Talk Session: Friday, August 15, 11:00 am – 12:00 pm, Room C1.03
Poster Session C: Friday, August 15, 2:00 – 5:00 pm, de Brug & E‑Hall

Reward-Prediction-Error-Guided Attention Explains Behavioral Learning Curves

Mingze Li Leukos¹, Grace W Lindsay¹; ¹New York University

Presenter: Mingze Li Leukos

Attention and learning are highly intertwined: past experiences determine where attention is focused at present and the focus of attention guides future experiences. In the context of reinforcement learning (RL), previous work has demonstrated how reward feedback can be used to learn a value function-based attention template (Jahn et al., 2024). Many open questions remain, however, regarding the exact way in which internal value estimates guide attentional modulation in the visual system. We explore these questions by building a perceptual model where top-down feature-selective attention is determined by an internal value function. We explore several different forms the relationship between value and attention can take in this model. We find that, to fit the unique features of the behaviorally-observed learning curve, attention should be focused on the color with highest estimated value and its strength should be inverted after large negative prediction errors. This work gives us a compact description of a latent process relating two important cognitive variables and sets the groundwork for exploring how the relationship between reward feedback and attention may vary under different tasks.

Topic Area: Reward, Value & Social Decision Making

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