Individual differences need not each get a model: a six-dimensional shared structure in 155 click-task participants replicates in 102 more
Differences between individuals in perceptual decision-making may be different mixtures of a few shared computational components. A low-rank neural network learned shared components and participant-specific weights jointly from trial-level choices; on the Bernoulli Clicks Task, out-of-sample prediction selected a six-dimensional representation of individual differences in the first dataset of 155 participants: two dimensions of temporal evidence weighting, two of choice-outcome history dependence, plus participant-specific side bias and signal-to-noise ratio. This low-dimensional organization reappeared in an independent replication dataset of 102 participants.