Abstract
The speed–accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time.
Highlights
Subjects in decision making experiments trade speed and accuracy at will (van Veen et al, 2008)
We model a decision circuit in the lateral intraparietal area (LIP) of posterior parietal cortex with a recurrent network model
2 Materials and Methods A cortical decision circuit was simulated with a network from a class of models widely used in population and firing rate simulations of cortical circuits (Wilson and Cowan, 1973; Pouget et al, 2000; Douglas and Martin, 2007), including feature maps in V1 (BenYishai et al, 1995), posterior parietal cortex (Salinas and Abbott, 1996; Standage et al, 2005), frontoparietal cortex (Cisek, 2006), and dorsolateral prefrontal cortex (Camperi and Wang, 1998)
Summary
Subjects in decision making experiments trade speed and accuracy at will (van Veen et al, 2008). Mutual inhibition ensures that the representation of evidence accumulating in each population comes at the expense of evidence accumulating in the other(s), implementing a subtractive operation (see Smith and Ratcliff, 2004; Bogacz, 2007). This neural framework instantiates a class of algorithms frequently referred to as the drift diffusion model (DDM), known to yield the fastest decisions for a given level of accuracy and the most accurate decisions for a given decision time (see Bogacz et al, 2006). Can the SAT be accomplished by neural populations with a fixed decision threshold?
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