Neural Measures of Human Decision Making Track Evidence Accumulation in Learned Space

Neural decision-making flexibly integrates evidence across sensory, mnemonic, and semantic domains. Yet prior demonstrations have focused on evidence that is either directly available in stimuli, retrieved from established representations, or computed relative to fixed perceptual frameworks. A fundamental question remains: does neural evidence accumulation extend to decisions based on evidence that must be computed through learned representational transformations? Here we show it does. We recorded scalp EEG while participants classified continuously-oriented visual stimuli into discrete categories defined by an experimenter-imposed boundary. Category-level evidence was operationalized as category coherence, or the angular distance between each stimulus and the learned boundary. We predicted that if neural decision mechanisms are truly domain-general, the centro-parietal positivity (CPP)--a scalp EEG potential indexing evidence accumulation--should scale with category coherence, and individual differences in CPP sensitivity should correlate with computational drift rates. Both predictions were supported: CPP slopes increased monotonically with category coherence, and individual differences in CPP slopes correlated with individual differences in drift rates across participants. These findings reveal that the brains decision machinery treats evidence identically regardless of its representational origin---whether externally available, pre-existing, or computed through learned transformations. Significance StatementEvidence accumulation is a universal principle by which brains convert information into decisions. Prior work demonstrates this mechanism for sensory evidence, memory retrieval, and evidence computed relative to fixed perceptual axes, but a critical gap remains: does it extend to decisions based entirely on learned, arbitrary rules? We show that it does. Specifically, we demonstrate that the centro-parietal positivity (CPP)--a neural marker of evidence accumulation--tracks decisions in rule-defined category space, with buildup rates that scale with distance from learned boundaries and correlate with computational measures of evidence-accumulation rate. This reveals that the brains decision machinery is flexible, adapting to evidence in any representational framework, whether externally available or internally constructed.