The gradual and noisy accumulation of evidence is a fundamental component of decision-making, with noise playing a key role as the source of variability and errors. However, the origins of this noise have never been determined. We developed decision-making tasks in which sensory evidence is delivered in randomly timed pulses, and analyzed the resulting data with models that use the richly detailed information of each trial's pulse timing to distinguish between different decision-making mechanisms. This analysis allowed measurement of the magnitude of noise in the accumulator's memory, separately from noise associated with incoming sensory evidence. In our tasks, the accumulator's memory was noiseless, for both rats and humans. In contrast, the addition of new sensory evidence was the primary source of variability. We suggest our task and modeling approach as a powerful method for revealing internal properties of decision-making processes. D ecisions in real life often need to be made based on noisy or unreliable evidence. Accumulating evidence from a set of noisy observations made over time makes it possible to average over different noise samples, thus improving estimates of the underlying signal. This principle is the basis for the influential class of "drift-diffusion" models (1-5), which have been broadly applied to explain a variety of phenomena in biology (6-8). Accumulation involves both maintaining a memory of evidence accrued so far and adding new evidence to the memory. Yet no test to date has distinguished between noise associated with each of these two components. We developed tasks in which subjects (humans and rats) were concurrently presented with two trains of pulses, one train representing "left"-labeled pulses and the other, "right"-labeled pulses. At the end of each trial, the subjects had to report which of the two trains had the greater total number of pulses. The timing of pulses was random and varied widely, both within and across individual trials (9, 10). We reasoned that the precisely known pulse timing would enable detailed modeling of the subjects' choices on each individual trial, whereas its variability would allow exploration of the stimulus space and would thus provide statistical power. In an auditory version of the task, performed by three humans and 19 rats, left pulse trains were clicks presented on a speaker to the left of the subject, and right pulse trains were clicks presented on a speaker to the right of the sub- †Corresponding author. E-mail: brody@princeton.edu Fig. 1 . Psychophysical tasks and summary of behavior. (A) Sequence of events in each trial of the rat auditory task. After light onset from a lightemitting diode (LED) in a center port, trained rats placed their nose into the port and "fixated" their nose there for a fixed amount of time until the light was turned off (1 to 2 s). Trains of randomly timed clicks were played concurrently from left and right free-field speakers during the last portion of the fixation time. After nose fixation and sounds ended, the rat made a choice, poking in the left or the right port to indicate which side played more clicks. Humans performed an analogous version of the task on a computer while wearing headphones. (B) Schematic diagram of a stimulus in the visual pulses version of the task, performed by humans on a computer. (C) Psychometric curves (fits to a four-parameter logistic function for each subject; see methods) for rat subjects. (D) Psychometric curves, as in (C), for human subjects. (E) Chronometric curves for an example rat. Difficulty is labeled by the ratio of click rates played on the two sides. For each difficulty, performance improves with longer stimulus durations. Dashed lines show the best-fit model predictions for this rat, as described in the text. The vertical axis shows mean accuracy and 95% confidence interval (CI).
The decision-making tasks are now linked with sensory evidence delivered in randomly timed pulses where noise is playing a key role as the source of variability and errors REF .
Rats and Humans Can Optimally Accumulate Evidence for Decision-Making
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