“… the quantitative performance of even highly trained humans and animals is suboptimal.”

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While the exact cause of neuronal noise is subject to debate8–11, it is thought that variability in neural firing impacts perceptual decision-mak-ing in one of two ways. First, noisy sensory information reduces the quality of the evidence going into the accumulator in the first place1,12–14. Second, noisy action selection causes mistakes to be made even after the integration process is complete15–17.The second source of suboptimality comes from the unequal weighting of evidence over time, which we call here the ‘integra-tion kernel’. In particular, while the optimal kernel in most percep-tual decision-making tasks is flat (that is, all information is weighed equally over time), a number of studies have shown that humans and animals can have quite suboptimal kernels. For example, in the random dot motion task, monkeys exhibit a ‘primacy’ kernel, put-ting more weight on the early parts of the stimulus relative to the later parts of the stimulus4. Conversely, in a slightly different inte-gration task, humans exhibit the opposite ‘recency’ kernel, weigh-ing later information more than early information18,19. Finally, in some experiments, this second source of suboptimality appears to be absent, with a ‘flat’ integration kernel being found in both rats and highly trained humans1.The third source of suboptimality reflects the tendency to let previous decisions and outcomes interfere with the present choice. Thus, when making multiple perceptual decisions, the current decision is influenced by the choice we just made; for example, by repeating an action when it is rewarded and choosing something else when it is not (a reinforcement learning effect8,15,16,20), or sim-ply repeating a choice regardless of the outcome associated with it (a choice kernel effect8,20,21). Such sequential dependence can be advantageous when there are temporal correlations between trials, as is the case in many reinforcement learning tasks15,16, but is subop-timal in most perceptual decision-making tasks when each trial is independent of the past8,20,22,23.Finally, the fourth suboptimality is an overall side bias where both humans and animals develop a preference for one option (for example, left) even though that leads to more errors overall

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