Rats and Humans Can Optimally Accumulate Evidence for Decision-Making

Abstract

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.

Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony

“…In studies of temporal integration over longer times, performance often improves with the square root of time [slope −1/2; (43)], rather than linearly with time (slope −1), as we find. However, temporal integration effects with slope −1/2 still can reflect perfect integration of sensory input (43,45) when the stimuli involved vary randomly, so that performance is limited by statistical variations in the stimulus. However, because those statistically varying stimuli are suprathreshold and are easily visible, and because linearity for the dim, constant visual stimuli described by Bloch's law becomes imperfect beyond ∼100 ms (42), studies of temporal integration over longer time periods might have a different neuronal circuit basis than our effects.…”

A Dynamic Approach to Recognition Memory

“…The model is nonetheless able to predict choice probabilities and response times quite well by allowing the drift rate and initial bias to vary randomly (according to particular distributions) between trials, although ballistic accumulator models are, by nature, unable to address situations in which the decision evidence changes over time (e.g., Tsetsos, Usher, & McClelland, 2011;Teodorescu & Usher, 2013); such situations provide evidence that competition between choice options-an integral feature of random walks-is necessary to explain observed decision behavior. While the assumption of non-interacting accumulators may not suffice to describe human decision making mechanisms, a model of evidence accumulation that assumes that noise only arises from the stimulus itself-i.e., not from the random perturbations assumed in a diffusion model-does a good job of explaining perceptual decision making in both humans and rats (Brunton, Botvinick, & Brody, 2013). Thus, the use of random perturbations in the diffusion model and of trial-to-trial stochastic drift rates in the linear ballistic model may simply be statistical descriptions of stimulus-driven noise, rather than inherent properties of decision making mechanisms.…”

Decision-related pupil dilation reflects upcoming choice and individual bias

“…Many perceptual decisions are not transient events but evolve gradually over several hundreds of milliseconds, due to the slow accumulation of noisy sensory information (27)(28)(29)(30)(31)(32)(33). Further, perceptual decisions are, like economic decisions (34), prone to strong biases that are not due to external asymmetries in the magnitude or probability of payoffs for certain choices.…”