A Spiking Neural Model of Decision Making and the Speed-Accuracy Trade-Off

The speed-accuracy trade-off (SAT) is the tendency for fast decisions to come at the expense of accurate performance. Evidence accumulation models such as the drift diffusion model can reproduce a variety of behavioral data related to the SAT, and their parameters have been linked to neural activities in the brain. However, our understanding of how biological neural networks realize the associated cognitive operations remains incomplete, limiting our ability to unify neurological and computational accounts of the SAT. We address this gap by developing and analyzing a biologically plausible spiking neural network that extends the drift diffusion approach. We apply our model to both perceptual and nonperceptual tasks, investigate several contextual manipulations, and validate model performance using neural and behavioral data. Behaviorally, we find that our model (a) reproduces individual response time distributions; (b) generalizes across experimental contexts, including the number of choice alternatives, speed- or accuracy-emphasis, and task difficulty; and (c) predicts accuracy data, despite being fit only to response time data. Neurally, we show that our model (a) recreates observed patterns of spiking neural activity and (b) captures age-related deficits that are consistent with the behavioral data. More broadly, our model exhibits the SAT across a variety of tasks and contexts and explains how individual differences in speed and accuracy arise from synaptic weights within a spiking neural network. Our work showcases a method for translating mathematical models into functional neural networks and demonstrates that simulating such networks permits analyses and predictions that are outside the scope of purely mathematical models.