Value-guided attention links what we learn to how much we learn

Abstract

Learning in uncertain environments requires identifying the relevant associations between stimuli, actions, and outcomes and determining how strongly to update these associations. Although often treated separately, these components likely interact in the brain. We hypothesized that this interaction shapes individual learning rates according to cue-choice alignment and reward outcome, thereby improving discrimination between competing cues. We tested this hypothesis using a probabilistic learning task in which human participants predicted outcomes based on multiple cues and reward feedback. We measured gaze and manipulated cue saliency to assess and influence which cues were preferentially processed during choice and feedback. Computational modeling revealed that learning rates were selectively enhanced for cues supporting the chosen option after reward and for cues opposing it after no reward. This learning-rate asymmetry based on cue-choice alignment sharpened discrimination among predictive cues, increased robustness to noise, and improved performance. Moreover, differential gaze toward supporting and opposing cues predicted this asymmetry, which was causally altered by manipulating cue saliency. Together, our results suggest that attention provides a unifying mechanism for coordinating what we learn from with how much we learn, helping preserve distinctions among competing cues and bringing several learning asymmetries within a common framework.